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Full Research Article

Genetic Divergence in Rice (Oryza sativa L.) Germplasms based on Agro-morphological Traits using Multivariate Analysis

S
Sourav Paramanik1
0009-0001-0628-4337
M
M. Subba Rao1,*
N
Niranjan Kumar Chaurasia2
0000-0002-6285-826X
S
Saraswati Pati3
0009-0000-8004-7009
V
Vishal Kumar Gupta1
0009-0001-9202-1911
1Department of Genetics and Plant Breeding, M.S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Paralakhemundi-761 211, Odisha, India.
2Department of Genetics and Plant Breeding, School of Agricultural Sciences, Nagaland University, Medziphema-797 106, Nagaland, India.
3Department of Biochemistry and Crop Physiology, M.S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Paralakhemundi-761 211, Odisha, India.

ABSTRACT

Background: A breeder’s success in rice breeding depends on identifying and using genetic diversity at genotypic level. This involves evaluating different traits to select parent genotypes with high genetic variability. The purpose of this study is to analyze genetic diversity and assess yield and yield attributes in the collected diverse rice germplasm.

Methods: The study aimed to assess genetic diversity and perform multivariate analysis of 16 grain yield and yield attributes among 50 rice genotypes. The experiment was conducted using Randomized Block Design with three replications at P.G. Research Farm, Ranadevi, MSSSoA, CUTM, Paralakhemundi, Odisha, during Kharif season of 2022-23.

Result: Cluster analysis categorized genotypes into six distinct clusters with Cluster I containing the most genotypes (40), followed by Cluster II (4) and Cluster V (3), while the rest had one genotype each. Cluster V exhibited the highest intra-cluster distance followed by Clusters II and I. The most significant inter-cluster distance was observed between Clusters V and II and then between Clusters VI and V, indicating substantial genetic divergence among these clusters. Grain yield plant-1 contributed the most to total genetic divergence followed by biological yield plant-1, 1000 grain weight and plant height. Five of 11 PCs analyzed had eigenvalues exceeding 1.0, accounting for 84.72% of the total cumulative variation. Based on per se performance, genotypes such as NLR 28523, NLR 9674, Khandagiri, Tulaipanji and Barsha emerged as genetically diverse, offering promise for breeding programs.

INTRODUCTION

Rice is the daily food for a significant portion of the world’s population particularly in Asia and has played a key role in human nutrition for the past 10,000 years (Zhao et al., 2020). It also comprises approximately 20 species with a fundamental chromosome number of 12. Oryza sativa L. is the primary variety cultivated, serving as the most crucial cereal food crop. Its cultivation is primarily concentrated in the wet season across a substantial geographic area. However, its productivity remains modest due to the scarcity of high-yield stable strains suitable for diverse seasons and agro-climatic zones throughout the nation. In 2022-23, global rice production reached 516.7 million metric tons, with China leading at 148.95 million, followed by India at 118.7 million and Indonesia at 34.6 million (FAO, 2023). Rice cultivation covered 165.25 million hectares globally with India having the largest area at 46.78 million hectares, followed by China at 30.50 million hectares. West Bengal was the top contributor in India, yielding 15.57 million tons followed by Uttar Pradesh (15.52) and Punjab (11.78) million tons (Zhang et al., 2017). Other significant contributors included Andhra Pradesh, Tamil Nadu, Bihar, Chhattisgarh and Odisha. Rice comes in various forms, such as long-grain fragrant Indian basmati, Thai jasmine, short-grain sticky sushi and Italian arborio (Chang et al., 2021). It also exists in various colors, including brown, black, red, white and golden rice enriched with vitamin A (Dubock, 2019). The conditions under which rice is cultivated vary, spanning from irrigated to rainfed and from lowland areas to higher altitudes.

Even though it is well known that rice hybrids have a very high level of heterosis for grain yield, their grain quality is a major issue, mostly due to the parental line’s highly varied grain quality (You et al., 2022; Abd El-Aty et al., 2022). Currently, there has been a growing emphasis on the breeding of quality traits, driven by increased consumer interest in rice quality. The preservation and exploitation of desirable variations in breeding programs, along with the selection of suitable parental lines, are crucial aspects of successful genetic diversity (Salgotra and Chauhan, 2023). Also, this approach enhances the understanding of rice developmental patterns. Principal component analysis is a vital and widely utilized method, rooted in true eigen vector-based multivariate analysis for precisely assessing genetic diversity.

Understanding the genetic divergence among rice varieties is essential for breeding programs to develop high-yielding, climate-resilient cultivars to ensure food security because of fast-changing environmental conditions and growing global demands (Rezvi et al., 2023). Several studies have aimed to investigate the extent of genetic variation among different rice lines, analyze the factors contributing to this diversity and elucidate its implications for rice breeding and crop improvement strategies (Swarup et al., 2021; Qin et al., 2021). Mahalanobis D2 statistical method is a useful technique for quantifying the extent of genetic variation among genotypes and connecting the clustering patterns to their geographical origins. Genetic divergence plays a crucial role in selecting parent lines for hybridization programs (Nivedha et al., 2024). Various researchers have explored genetic diversity, clustering patterns and the contribution of different traits to divergence and selection effectiveness (Tarunum et al., 2023; Sujitha et al., 2020). Multivariate analysis techniques offer a powerful toolset for dissecting complex datasets, providing insights into the intricate relationships between various agronomic traits, environmental factors and genetic markers within rice populations (Shrestha et al., 2021). PCA emerges as data reduction technique, a true virtuoso in exploring intricate interdependencies. To elegantly simplify the intricate and multifaceted connections between sets of measured traits by revealing common threads and shared characteristics among independent variables. The objectives of this study are to assess the genetic divergence and perform multivariate analysis among 50 selected rice genotypes to identify suitable parental lines for hybridization programs and to enhance genetic parameters influencing yield attributes.

MATERIALS AND METHODS

Experimental location
 
The investigation was conducted at P.G. Research Farm, Ranadevi, MSSSoA, CUTM, Paralakhemundi, Odisha, during Kharif 2022-23 season. The experimental site is situated in the North Eastern Ghat zone of Odisha at 18.77027oN latitude and 84.10195oE longitude, at an elevation of 57 meters above sea level. The soil is well-drained, with a sandy loam texture and pH of around 7.4.
 
Genetic materials and planting details
 
The test entries consisted of 50 selected rice accessions from different regions of Andhra Pradesh, Odisha and West Bengal. Twenty-seven days old seedlings were transplanted in randomized block design (RBD) with three replicated plots using  20 x 15 cm spacing. Recommended practices and packages were followed during the crop period under transplanted conditions to ensure good crop growth.
 
Observations recorded
 
The observations were collected from randomly selected ten plants from each replication for 16 quantitative traits as per the objectives of the study. The data was recorded on plant height (PH), number of productive tillers plant-1 (NPT), panicle length (PL), filled grains panicle-1 (FGP), total grains panicle-1 (TGP), spikelet fertility percentage (SF), 1000 grain weight (TW), straw yield plant-1 (SYP), biological yield plant-1 (BYP), grain yield plant-1 (GYP) and harvest index (HI). The plants from the border were excluded to minimize the border effect. Days to 50% flowering (DFF) and days to maturity (DM) were recorded on plot basis. Randomly, 10 rice grains were selected to assess their dimensions, including grain length (GL), grain breadth (GB) and grain length to breadth ratio (LB), using a vernier calliper on a set of chosen grains.
 
Statistical analyses
 
The statistical analysis utilized the average data from each replication employing RBD design to determine the significance of genotypic variance across multiple genotypes as recommended by Rao (1952). The genetic divergence among the genotypes for 16 morphological characters was assessed using Mahalanobis D2 statistics. This analysis involved evaluating data collected for various traits following the methodologies established by Mahalanobis (1936) and the clustering pattern described by Ward (1963). Principal component analysis, a widely used method for analyzing multivariate data, was initially introduced by Pearson (1901) and developed by Hotelling (1933).

RESULTS AND DISCUSSION

An effective categorization of test entries based on distinct characteristics is essential for distinguishing genetically similar and dissimilar genotypes, a fundamental requirement for any genetic study. The analysis of the variance indicated distinctions among the different genetic types across all traits under investigation.
 
Grouping of genotypes into clusters
 
Using Tocher’s method, 50 selected rice genotypes were divided into six distinct clusters as depicted in Table 1. The D2 values were derived from the average of genotypes. The cluster dendrogram shows the variety of genotypes within the plant population studied (Fig 1). Among them, the total number of genotypes in Cluster I was highest (40), followed by Cluster II (4) and Cluster V (3). Clusters III (Barsha), V (NLR 9674) and VI (Tulaipanji) stood out as solitary clusters. Kushwaha et al., (2020) and Singh et al., (2022) found similar results about the non-association between geographical regions and genetic diversity. According to Rezk et al., (2024) and Sheeba et al., (2023), various traits contributed to genetic differences with no correlation observed between the origin of the genotypes and their genetic diversity.

Table 1: Clustering pattern of 50 rice genotypes for yield and its contributing traits using Tocher’s method.



Fig 1: Dendrogram depicting the clustering of rice genotypes based on yield and its attributing traits.


 
Average intra and inter-cluster distances
 
The intra-cluster distances varied from zero (Clusters III, IV and VI) to 200.11 (Cluster V). The greatest inter-cluster distance (833.28) was observed between Cluster V and II followed by Cluster VI and V (499.65), Cluster VI and IV (462.52), Cluster IV and II (427.44), Cluster III and II (402.5) and Cluster VI and III (371.2) indicating substantial genetic diversity among genotypes across these clusters (Table 2 and Fig 2). Crosses from genotypes in Cluster I, II and V exhibiting significant divergence are likely to yield more favorable offspring for achieving increased yield through genetic diversity (Chhodavadiya et al., 2023; Thakur and Sarma, 2023).

Table 2: Average intra (diagonal) and inter (off-diagonal) cluster distances among six clusters of rice genotypes for yield and its contributing traits.



Fig 2: Diagram of intra- and inter-cluster distance (D2 values) among six clusters using Tocher’s method.



The analysis of genetically divergent clusters and the computation of distances (D2 values) among the studied genotypes of rice are given in Table 3. Upon careful examination of these distances, it was observed that NLR 28523 in Cluster V and Khandagiri in Cluster II showed the highest genotypic distance (1742.78). Similar patterns of significant genetic distances between rice lines were identified in other clusters, such as Tulaipanji in Cluster VI and NLR 28523 in Cluster V (992.17). Tulaipanji in Cluster VI and NLR 9674 in Cluster IV demonstrated a considerable genotypic distance (627.20). Also, NLR 9674 in Cluster IV and Khandagiri in Cluster II exhibited a considerable genotypic distance (803.85). Additionally, Barsha in Cluster III and Khandagiri in Cluster II showed D2 distance (691.12) and Tulaipanji in Cluster VI and Barsha in Cluster III recorded a substantial genotypic distance (387.07). Identifying parental lines from these different groups has great potential for breeding programs. Crossing parents with greater genetic divergence can generate higher variability and strong beneficial traits in their offspring. These results were also in accordance with some previous studies (Mondal et al., 2024; Roy et al., 2023;2024).

Table 3: Description of genetically divergent clusters and D2 distances among selected rice genotypes for yield and its contributing traits.


 
Cluster means
 
Table 4 shows the cluster means for DFF and DM, which were highest in Cluster IV (150.67 and 180.67) and lowest in Cluster II (96.08 and 126.33). Similarly, the cluster mean for PH was highest in Cluster III (158.26) and lowest in Cluster II (109.38). The NPT exhibited highest mean value in Cluster VI (20.33) and lowest in Cluster III (9.60), while PL had its highest mean in Cluster VI (27.00) and lowest in Clusters II and III (22.49). For TW, Cluster III (26.97) recorded highest mean, while Cluster V (16.26) had lowest. The highest mean for TGP was in Cluster V (240.60), whereas lowest was in Cluster II (96.28). Cluster V (202.99) exhibited highest mean for FGP, whereas Cluster VI (53.27) had lowest. SF had the maximum mean in Cluster II (97.44) and Cluster III (60.57) had minimum value. GYP and BYP had highest mean in Cluster IV (36.31 and 84.86) and lowest in Cluster VI (20.03 and 46.10). HI and LB had highest mean in Cluster II (4.45 and 0.52) and lowest in Cluster IV (2.78 and 0.43). GL had highest mean in Cluster II (9.85) and lowest in Cluster V (6.68), while GB had highest mean in Cluster III (2.69) and lowest mean in Cluster VI (1.92). Cluster IV (48.55) exhibited highest mean for SYP, whereas Cluster II (22.28) showed lowest mean. The findings demonstrated significant genetic divergence within the genotypes in these groups, suggesting their potential utility in targeted trait enhancement in plant breeding initiatives. Crossing genotypes within these clusters is also expected to produce substantial heterosis. This result aligns with earlier studies by Pavankumar et al., (2022) and Sudeepthi et al., (2020).

Table 4: Cluster mean values for yield and its contributing traits of rice genotypes grouped by Tocher’s method.


 
Contribution towards total divergence
 
The highest percentage contribution towards genetic divergence was observed in GYP (16.78%), followed by BYP (10.43%), TW (8.9%), PH (8.49%), HI (7.43%), SYP (7.33%), PL (6.42%), NPT (6.33%), SF (4.98%), GL (4.32%), LB (3.5%), GB (3.44%), FGP (3.43%), DFF (3.21%), DM (2.57%) and TGP (2.44%), as shown in Table 5 and Fig 3. All genotypes exhibited significant variability in economic traits especially in GYP suggesting the need for further study on allelic characterization. Previous studies also found similar results by different researchers (Bhargavi et al., 2023; Bora et al., 2023; Shrivastav et al., 2022).

Table 5: Contribution of yield and its related traits towards total genetic divergence among rice genotypes.


 
Principal component analysis
 
Principal component analysis serves to condense large datasets into more manageable principal components, preserving all crucial details by analyzing the correlations between variables. The eigenvector values, variation percentages and cumulative percentages are presented in Table 6. Among the eleven components, five PCs observed eigenvalues surpassing 1.0, thus contributing significantly to the cumulative variability of 84.72% across the considered variables. PC1, accounting for 36.97% of the total variation, along with the rest of PCs, contributed 56.05%, 67.52%, 78.15% and 84.72% to the overall variance. Components boasting multiple eigenvalues showcased heightened variability among rice genotypes, facilitating the identification of diverse parental selections. The scree plot provided insights into the percentage of variance explained by eigenvalues and principal components (Fig 4). In this study, PC1 showed 36.97% variability, characterized by an eigenvalue of 5.92. The graph depicts PC1 as exhibiting the highest degree of variability compared to other PCs. One earlier research reported a maximum variance of 38.72% in PC1 across 49 rice lines (Christina et al., 2021). Thus, selecting genotypes from PC1 could prove advantageous for future breeding endeavors focused on improving traits (Tiwari et al., 2022).

Fig 4: Scree plot depicting the clustering of genotypes based on yield and its attributing traits.



Table 6: Eigenvalues, percent of variance and cumulative variance of yield and its contributing traits among rice genotypes.



In this study, the biplot is constructed using PC1 and PC2 to analyze the associations between 50 rice accessions based on yield and its related attributes (Fig 5). Biplot based correlations among traits explained 56.05% of the total variation, providing a reliable estimate for evaluating their impact on yield and inter-similarities. All characters except TGP, SF, NPT and HI have higher vector lengths, indicating a more substantial influence on the variation in a particular dimension. The loading plot revealed substantial diversity among nearly all genotypes and variables. These findings are consistent with those reported by Sheela et al., (2020).

Fig 5: Biplot presenting the relationship between yield and its attributing traits among rice genotypes.



The contribution of 16 quantitative traits to the principal components is presented in Table 7. In PC1, SYP (0.913) followed by BYP (0.908), DM (0.890), DFF (0.881), GYP (0.780), FGP (0.763), PH (0.556), TGP (0.500), GB (0.441), PL (0.300) and SF (0.184) showed favourable loadings, while the remaining traits exhibited negative loadings. PC2 showed positive loadings for parameters such as TW (0.888), GL (0.754), NPT (0.484) and HI (0.434) with other factors such as FGP (-0.493) followed by SF (-0.416) and TGP (-0.194) demonstrating negative loadings. Traits in PC3 that recorded positive loadings were LB (0.579), SF (0.572), NPT (0.417), SYP (0.301), GL (0.292), BYP (0.203), DFF (0.120), DM (0.111) and GYP (0.043), while the remaining characters observed negative loadings. In PC4, variables such as PL (0.613), NPT (0.448), TGP (0.415), PH (0.332), LB (0.126) and SYP (0.026) exhibited positive loading values and the remaining traits showed negative loadings. In PC5, TGP (0.502) followed by LB (0.398), FGP (0.287) and GL (0.258) exhibited positive loadings, while PH (-0.418), followed by GB (-0.295), SF (-0.228) and NPT (-0.148) showed negative loadings. Among the studied traits, PC1 and PC2 contributed more to genetic divergence and accounted for a significant portion of the variability. Therefore, selecting traits with substantial variability will benefit future breeding endeavors.

Table 7: Five PCs along with their factor loading values for yield and its contributing traits among rice genotypes.

CONCLUSION

The examination of variance indicated significant diversity among the 50 rice genotypes across all traits. Utilizing D2 analysis, the genotypes were categorized into six clusters. Considering the inter-cluster distances, genotypes from Cluster V and II, followed by Cluster VI and V, Cluster VI and IV are suggested as potential parents for upcoming hybridization endeavors. GYP, BYP, TW and PH were identified as pivotal contributors to overall divergence, underscoring their importance for future crop improvements. The PCA of the selected genotypes indicated that the major five PCs, with eigenvalues larger than one explained 84.72% of the total cumulative variance. This found a strong connection between the traits evaluated. Based on analysis of cluster means and inter-cluster distance, the genotypes NLR 28523, NLR 9674, Khandagiri, Tulaipanji and Barsha showed promise for their utilization in breeding programs aimed at producing high-yielding rice varieties with desirable quality traits.

ACKNOWLEDGEMENT

The authors sincerely appreciate the technical support and access to facilities provided by the Department of Genetics and Plant Breeding at M.S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Paralakhemundi, Odisha, India.
 
Disclaimers
 
The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect losses resulting from the use of this content.
 
Informed consent
 
All animal procedures for experiments were approved by the Committee of Experimental Animal Care and handling techniques were approved by the University of Animal Care Committee.

CONFLICT OF INTEREST

The authors declare that there are no conflicts of interest regarding the publication of this article. No funding or sponsorship influenced the design of the study, data collection, analysis, decision to publish or preparation of the manuscript.

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    Genetic Divergence in Rice (Oryza sativa L.) Germplasms based on Agro-morphological Traits using Multivariate Analysis

    S
    Sourav Paramanik1
    0009-0001-0628-4337
    M
    M. Subba Rao1,*
    N
    Niranjan Kumar Chaurasia2
    0000-0002-6285-826X
    S
    Saraswati Pati3
    0009-0000-8004-7009
    V
    Vishal Kumar Gupta1
    0009-0001-9202-1911
    1Department of Genetics and Plant Breeding, M.S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Paralakhemundi-761 211, Odisha, India.
    2Department of Genetics and Plant Breeding, School of Agricultural Sciences, Nagaland University, Medziphema-797 106, Nagaland, India.
    3Department of Biochemistry and Crop Physiology, M.S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Paralakhemundi-761 211, Odisha, India.

    ABSTRACT

    Background: A breeder’s success in rice breeding depends on identifying and using genetic diversity at genotypic level. This involves evaluating different traits to select parent genotypes with high genetic variability. The purpose of this study is to analyze genetic diversity and assess yield and yield attributes in the collected diverse rice germplasm.

    Methods: The study aimed to assess genetic diversity and perform multivariate analysis of 16 grain yield and yield attributes among 50 rice genotypes. The experiment was conducted using Randomized Block Design with three replications at P.G. Research Farm, Ranadevi, MSSSoA, CUTM, Paralakhemundi, Odisha, during Kharif season of 2022-23.

    Result: Cluster analysis categorized genotypes into six distinct clusters with Cluster I containing the most genotypes (40), followed by Cluster II (4) and Cluster V (3), while the rest had one genotype each. Cluster V exhibited the highest intra-cluster distance followed by Clusters II and I. The most significant inter-cluster distance was observed between Clusters V and II and then between Clusters VI and V, indicating substantial genetic divergence among these clusters. Grain yield plant-1 contributed the most to total genetic divergence followed by biological yield plant-1, 1000 grain weight and plant height. Five of 11 PCs analyzed had eigenvalues exceeding 1.0, accounting for 84.72% of the total cumulative variation. Based on per se performance, genotypes such as NLR 28523, NLR 9674, Khandagiri, Tulaipanji and Barsha emerged as genetically diverse, offering promise for breeding programs.

    INTRODUCTION

    Rice is the daily food for a significant portion of the world’s population particularly in Asia and has played a key role in human nutrition for the past 10,000 years (Zhao et al., 2020). It also comprises approximately 20 species with a fundamental chromosome number of 12. Oryza sativa L. is the primary variety cultivated, serving as the most crucial cereal food crop. Its cultivation is primarily concentrated in the wet season across a substantial geographic area. However, its productivity remains modest due to the scarcity of high-yield stable strains suitable for diverse seasons and agro-climatic zones throughout the nation. In 2022-23, global rice production reached 516.7 million metric tons, with China leading at 148.95 million, followed by India at 118.7 million and Indonesia at 34.6 million (FAO, 2023). Rice cultivation covered 165.25 million hectares globally with India having the largest area at 46.78 million hectares, followed by China at 30.50 million hectares. West Bengal was the top contributor in India, yielding 15.57 million tons followed by Uttar Pradesh (15.52) and Punjab (11.78) million tons (Zhang et al., 2017). Other significant contributors included Andhra Pradesh, Tamil Nadu, Bihar, Chhattisgarh and Odisha. Rice comes in various forms, such as long-grain fragrant Indian basmati, Thai jasmine, short-grain sticky sushi and Italian arborio (Chang et al., 2021). It also exists in various colors, including brown, black, red, white and golden rice enriched with vitamin A (Dubock, 2019). The conditions under which rice is cultivated vary, spanning from irrigated to rainfed and from lowland areas to higher altitudes.

    Even though it is well known that rice hybrids have a very high level of heterosis for grain yield, their grain quality is a major issue, mostly due to the parental line’s highly varied grain quality (You et al., 2022; Abd El-Aty et al., 2022). Currently, there has been a growing emphasis on the breeding of quality traits, driven by increased consumer interest in rice quality. The preservation and exploitation of desirable variations in breeding programs, along with the selection of suitable parental lines, are crucial aspects of successful genetic diversity (Salgotra and Chauhan, 2023). Also, this approach enhances the understanding of rice developmental patterns. Principal component analysis is a vital and widely utilized method, rooted in true eigen vector-based multivariate analysis for precisely assessing genetic diversity.

    Understanding the genetic divergence among rice varieties is essential for breeding programs to develop high-yielding, climate-resilient cultivars to ensure food security because of fast-changing environmental conditions and growing global demands (Rezvi et al., 2023). Several studies have aimed to investigate the extent of genetic variation among different rice lines, analyze the factors contributing to this diversity and elucidate its implications for rice breeding and crop improvement strategies (Swarup et al., 2021; Qin et al., 2021). Mahalanobis D2 statistical method is a useful technique for quantifying the extent of genetic variation among genotypes and connecting the clustering patterns to their geographical origins. Genetic divergence plays a crucial role in selecting parent lines for hybridization programs (Nivedha et al., 2024). Various researchers have explored genetic diversity, clustering patterns and the contribution of different traits to divergence and selection effectiveness (Tarunum et al., 2023; Sujitha et al., 2020). Multivariate analysis techniques offer a powerful toolset for dissecting complex datasets, providing insights into the intricate relationships between various agronomic traits, environmental factors and genetic markers within rice populations (Shrestha et al., 2021). PCA emerges as data reduction technique, a true virtuoso in exploring intricate interdependencies. To elegantly simplify the intricate and multifaceted connections between sets of measured traits by revealing common threads and shared characteristics among independent variables. The objectives of this study are to assess the genetic divergence and perform multivariate analysis among 50 selected rice genotypes to identify suitable parental lines for hybridization programs and to enhance genetic parameters influencing yield attributes.

    MATERIALS AND METHODS

    Experimental location
     
    The investigation was conducted at P.G. Research Farm, Ranadevi, MSSSoA, CUTM, Paralakhemundi, Odisha, during Kharif 2022-23 season. The experimental site is situated in the North Eastern Ghat zone of Odisha at 18.77027oN latitude and 84.10195oE longitude, at an elevation of 57 meters above sea level. The soil is well-drained, with a sandy loam texture and pH of around 7.4.
     
    Genetic materials and planting details
     
    The test entries consisted of 50 selected rice accessions from different regions of Andhra Pradesh, Odisha and West Bengal. Twenty-seven days old seedlings were transplanted in randomized block design (RBD) with three replicated plots using  20 x 15 cm spacing. Recommended practices and packages were followed during the crop period under transplanted conditions to ensure good crop growth.
     
    Observations recorded
     
    The observations were collected from randomly selected ten plants from each replication for 16 quantitative traits as per the objectives of the study. The data was recorded on plant height (PH), number of productive tillers plant-1 (NPT), panicle length (PL), filled grains panicle-1 (FGP), total grains panicle-1 (TGP), spikelet fertility percentage (SF), 1000 grain weight (TW), straw yield plant-1 (SYP), biological yield plant-1 (BYP), grain yield plant-1 (GYP) and harvest index (HI). The plants from the border were excluded to minimize the border effect. Days to 50% flowering (DFF) and days to maturity (DM) were recorded on plot basis. Randomly, 10 rice grains were selected to assess their dimensions, including grain length (GL), grain breadth (GB) and grain length to breadth ratio (LB), using a vernier calliper on a set of chosen grains.
     
    Statistical analyses
     
    The statistical analysis utilized the average data from each replication employing RBD design to determine the significance of genotypic variance across multiple genotypes as recommended by Rao (1952). The genetic divergence among the genotypes for 16 morphological characters was assessed using Mahalanobis D2 statistics. This analysis involved evaluating data collected for various traits following the methodologies established by Mahalanobis (1936) and the clustering pattern described by Ward (1963). Principal component analysis, a widely used method for analyzing multivariate data, was initially introduced by Pearson (1901) and developed by Hotelling (1933).

    RESULTS AND DISCUSSION

    An effective categorization of test entries based on distinct characteristics is essential for distinguishing genetically similar and dissimilar genotypes, a fundamental requirement for any genetic study. The analysis of the variance indicated distinctions among the different genetic types across all traits under investigation.
     
    Grouping of genotypes into clusters
     
    Using Tocher’s method, 50 selected rice genotypes were divided into six distinct clusters as depicted in Table 1. The D2 values were derived from the average of genotypes. The cluster dendrogram shows the variety of genotypes within the plant population studied (Fig 1). Among them, the total number of genotypes in Cluster I was highest (40), followed by Cluster II (4) and Cluster V (3). Clusters III (Barsha), V (NLR 9674) and VI (Tulaipanji) stood out as solitary clusters. Kushwaha et al., (2020) and Singh et al., (2022) found similar results about the non-association between geographical regions and genetic diversity. According to Rezk et al., (2024) and Sheeba et al., (2023), various traits contributed to genetic differences with no correlation observed between the origin of the genotypes and their genetic diversity.

    Table 1: Clustering pattern of 50 rice genotypes for yield and its contributing traits using Tocher’s method.



    Fig 1: Dendrogram depicting the clustering of rice genotypes based on yield and its attributing traits.


     
    Average intra and inter-cluster distances
     
    The intra-cluster distances varied from zero (Clusters III, IV and VI) to 200.11 (Cluster V). The greatest inter-cluster distance (833.28) was observed between Cluster V and II followed by Cluster VI and V (499.65), Cluster VI and IV (462.52), Cluster IV and II (427.44), Cluster III and II (402.5) and Cluster VI and III (371.2) indicating substantial genetic diversity among genotypes across these clusters (Table 2 and Fig 2). Crosses from genotypes in Cluster I, II and V exhibiting significant divergence are likely to yield more favorable offspring for achieving increased yield through genetic diversity (Chhodavadiya et al., 2023; Thakur and Sarma, 2023).

    Table 2: Average intra (diagonal) and inter (off-diagonal) cluster distances among six clusters of rice genotypes for yield and its contributing traits.



    Fig 2: Diagram of intra- and inter-cluster distance (D2 values) among six clusters using Tocher’s method.



    The analysis of genetically divergent clusters and the computation of distances (D2 values) among the studied genotypes of rice are given in Table 3. Upon careful examination of these distances, it was observed that NLR 28523 in Cluster V and Khandagiri in Cluster II showed the highest genotypic distance (1742.78). Similar patterns of significant genetic distances between rice lines were identified in other clusters, such as Tulaipanji in Cluster VI and NLR 28523 in Cluster V (992.17). Tulaipanji in Cluster VI and NLR 9674 in Cluster IV demonstrated a considerable genotypic distance (627.20). Also, NLR 9674 in Cluster IV and Khandagiri in Cluster II exhibited a considerable genotypic distance (803.85). Additionally, Barsha in Cluster III and Khandagiri in Cluster II showed D2 distance (691.12) and Tulaipanji in Cluster VI and Barsha in Cluster III recorded a substantial genotypic distance (387.07). Identifying parental lines from these different groups has great potential for breeding programs. Crossing parents with greater genetic divergence can generate higher variability and strong beneficial traits in their offspring. These results were also in accordance with some previous studies (Mondal et al., 2024; Roy et al., 2023;2024).

    Table 3: Description of genetically divergent clusters and D2 distances among selected rice genotypes for yield and its contributing traits.


     
    Cluster means
     
    Table 4 shows the cluster means for DFF and DM, which were highest in Cluster IV (150.67 and 180.67) and lowest in Cluster II (96.08 and 126.33). Similarly, the cluster mean for PH was highest in Cluster III (158.26) and lowest in Cluster II (109.38). The NPT exhibited highest mean value in Cluster VI (20.33) and lowest in Cluster III (9.60), while PL had its highest mean in Cluster VI (27.00) and lowest in Clusters II and III (22.49). For TW, Cluster III (26.97) recorded highest mean, while Cluster V (16.26) had lowest. The highest mean for TGP was in Cluster V (240.60), whereas lowest was in Cluster II (96.28). Cluster V (202.99) exhibited highest mean for FGP, whereas Cluster VI (53.27) had lowest. SF had the maximum mean in Cluster II (97.44) and Cluster III (60.57) had minimum value. GYP and BYP had highest mean in Cluster IV (36.31 and 84.86) and lowest in Cluster VI (20.03 and 46.10). HI and LB had highest mean in Cluster II (4.45 and 0.52) and lowest in Cluster IV (2.78 and 0.43). GL had highest mean in Cluster II (9.85) and lowest in Cluster V (6.68), while GB had highest mean in Cluster III (2.69) and lowest mean in Cluster VI (1.92). Cluster IV (48.55) exhibited highest mean for SYP, whereas Cluster II (22.28) showed lowest mean. The findings demonstrated significant genetic divergence within the genotypes in these groups, suggesting their potential utility in targeted trait enhancement in plant breeding initiatives. Crossing genotypes within these clusters is also expected to produce substantial heterosis. This result aligns with earlier studies by Pavankumar et al., (2022) and Sudeepthi et al., (2020).

    Table 4: Cluster mean values for yield and its contributing traits of rice genotypes grouped by Tocher’s method.


     
    Contribution towards total divergence
     
    The highest percentage contribution towards genetic divergence was observed in GYP (16.78%), followed by BYP (10.43%), TW (8.9%), PH (8.49%), HI (7.43%), SYP (7.33%), PL (6.42%), NPT (6.33%), SF (4.98%), GL (4.32%), LB (3.5%), GB (3.44%), FGP (3.43%), DFF (3.21%), DM (2.57%) and TGP (2.44%), as shown in Table 5 and Fig 3. All genotypes exhibited significant variability in economic traits especially in GYP suggesting the need for further study on allelic characterization. Previous studies also found similar results by different researchers (Bhargavi et al., 2023; Bora et al., 2023; Shrivastav et al., 2022).

    Table 5: Contribution of yield and its related traits towards total genetic divergence among rice genotypes.


     
    Principal component analysis
     
    Principal component analysis serves to condense large datasets into more manageable principal components, preserving all crucial details by analyzing the correlations between variables. The eigenvector values, variation percentages and cumulative percentages are presented in Table 6. Among the eleven components, five PCs observed eigenvalues surpassing 1.0, thus contributing significantly to the cumulative variability of 84.72% across the considered variables. PC1, accounting for 36.97% of the total variation, along with the rest of PCs, contributed 56.05%, 67.52%, 78.15% and 84.72% to the overall variance. Components boasting multiple eigenvalues showcased heightened variability among rice genotypes, facilitating the identification of diverse parental selections. The scree plot provided insights into the percentage of variance explained by eigenvalues and principal components (Fig 4). In this study, PC1 showed 36.97% variability, characterized by an eigenvalue of 5.92. The graph depicts PC1 as exhibiting the highest degree of variability compared to other PCs. One earlier research reported a maximum variance of 38.72% in PC1 across 49 rice lines (Christina et al., 2021). Thus, selecting genotypes from PC1 could prove advantageous for future breeding endeavors focused on improving traits (Tiwari et al., 2022).

    Fig 4: Scree plot depicting the clustering of genotypes based on yield and its attributing traits.



    Table 6: Eigenvalues, percent of variance and cumulative variance of yield and its contributing traits among rice genotypes.



    In this study, the biplot is constructed using PC1 and PC2 to analyze the associations between 50 rice accessions based on yield and its related attributes (Fig 5). Biplot based correlations among traits explained 56.05% of the total variation, providing a reliable estimate for evaluating their impact on yield and inter-similarities. All characters except TGP, SF, NPT and HI have higher vector lengths, indicating a more substantial influence on the variation in a particular dimension. The loading plot revealed substantial diversity among nearly all genotypes and variables. These findings are consistent with those reported by Sheela et al., (2020).

    Fig 5: Biplot presenting the relationship between yield and its attributing traits among rice genotypes.



    The contribution of 16 quantitative traits to the principal components is presented in Table 7. In PC1, SYP (0.913) followed by BYP (0.908), DM (0.890), DFF (0.881), GYP (0.780), FGP (0.763), PH (0.556), TGP (0.500), GB (0.441), PL (0.300) and SF (0.184) showed favourable loadings, while the remaining traits exhibited negative loadings. PC2 showed positive loadings for parameters such as TW (0.888), GL (0.754), NPT (0.484) and HI (0.434) with other factors such as FGP (-0.493) followed by SF (-0.416) and TGP (-0.194) demonstrating negative loadings. Traits in PC3 that recorded positive loadings were LB (0.579), SF (0.572), NPT (0.417), SYP (0.301), GL (0.292), BYP (0.203), DFF (0.120), DM (0.111) and GYP (0.043), while the remaining characters observed negative loadings. In PC4, variables such as PL (0.613), NPT (0.448), TGP (0.415), PH (0.332), LB (0.126) and SYP (0.026) exhibited positive loading values and the remaining traits showed negative loadings. In PC5, TGP (0.502) followed by LB (0.398), FGP (0.287) and GL (0.258) exhibited positive loadings, while PH (-0.418), followed by GB (-0.295), SF (-0.228) and NPT (-0.148) showed negative loadings. Among the studied traits, PC1 and PC2 contributed more to genetic divergence and accounted for a significant portion of the variability. Therefore, selecting traits with substantial variability will benefit future breeding endeavors.

    Table 7: Five PCs along with their factor loading values for yield and its contributing traits among rice genotypes.

    CONCLUSION

    The examination of variance indicated significant diversity among the 50 rice genotypes across all traits. Utilizing D2 analysis, the genotypes were categorized into six clusters. Considering the inter-cluster distances, genotypes from Cluster V and II, followed by Cluster VI and V, Cluster VI and IV are suggested as potential parents for upcoming hybridization endeavors. GYP, BYP, TW and PH were identified as pivotal contributors to overall divergence, underscoring their importance for future crop improvements. The PCA of the selected genotypes indicated that the major five PCs, with eigenvalues larger than one explained 84.72% of the total cumulative variance. This found a strong connection between the traits evaluated. Based on analysis of cluster means and inter-cluster distance, the genotypes NLR 28523, NLR 9674, Khandagiri, Tulaipanji and Barsha showed promise for their utilization in breeding programs aimed at producing high-yielding rice varieties with desirable quality traits.

    ACKNOWLEDGEMENT

    The authors sincerely appreciate the technical support and access to facilities provided by the Department of Genetics and Plant Breeding at M.S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Paralakhemundi, Odisha, India.
     
    Disclaimers
     
    The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect losses resulting from the use of this content.
     
    Informed consent
     
    All animal procedures for experiments were approved by the Committee of Experimental Animal Care and handling techniques were approved by the University of Animal Care Committee.

    CONFLICT OF INTEREST

    The authors declare that there are no conflicts of interest regarding the publication of this article. No funding or sponsorship influenced the design of the study, data collection, analysis, decision to publish or preparation of the manuscript.

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